| // ============================================================================ | |
| // FTM_OPENING_RANGE_BREAKOUT_MNQ_v1_8_0_RC3 | |
| // | |
| // One self-contained MNQ opening-range breakout strategy. It requires one-minute | |
| // MNQ bars and the CME US Index Futures ETH Trading Hours template. All trading | |
| // decisions use New York time after converting NinjaTrader timestamps through | |
| // UTC, so the NinjaTrader display time zone may be Eastern, UTC, Madrid, or any | |
| // other correctly configured system time zone. | |
| // | |
| // The strategy builds the 09:30-09:45 ET opening range, then checks completed |
| // ==UserScript== | |
| // @name meshy model dwonloader | |
| // @namespace https://kirakira.cloud | |
| // @version 1.0.0 | |
| // @description meshy model downloader, bypass paywall, made by the poor for the poor | |
| // @author kirakira | |
| // @match https://www.meshy.ai/* | |
| // @run-at document-start | |
| // @grant none | |
| // ==/UserScript== |
| VibeCoded AI-Slop License v1.0 | |
| Copyright (c) [YEAR] [AUTHOR] | |
| This software, source code, documentation, configuration, generated artifacts, | |
| and whatever else happened to end up in this repository (collectively, the | |
| "Software") is released under the VibeCoded AI-Slop License v1.0. | |
| 1. Permission |
This guide documents the process to optimize and run large language models on AMD APUs (specifically 7840U with Radeon 780M, Ayaneo Flip w/ 32GB RAM) using llama.cpp with Vulkan acceleration.
# Disable read-only filesystem (SteamOS specific)
sudo steamos-readonly disable
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
The standard for building software with AI agents.
This is the compact, operational version of The Proof of Done Manifesto. It is designed to be pasted into AGENTS.md, CLAUDE.md, or the context of a coding agent.
It does not replace tests, review, CI, or product validation. It defines what an agent must show before it may claim that software work is done.
Best AI Quiz Generator for Students in 2026 – Detailed & Unique Guide
Students today deal with more study material than ever — lecture slides, dense PDFs, handwritten notes, YouTube recordings, and online resources. The biggest challenge is not finding information. It is turning that information into effective practice questions that actually prepare you for exams.
Manually writing quizzes is slow and often incomplete. AI quiz generators solve this by converting your own materials into high-quality practice tests within minutes. The right tool can save hours every week and improve retention through active recall.
In this detailed 2026 guide, we rank the best AI quiz generators for students using a fresh set of competitors. Evaluation is based on: